Many industrial noise environments are characterized by high levels of impact noise that are superimposed on a continuous background noise thus producing a very complex temporal signal. The limited demographic data and an increasing body of experimental animal data show that many of these "complex noise" environments pose an unusually high risk of hearing loss to the repeatedly exposed individual. The need exists to develop metrics which can be extracted from such "complex noise" environments and used to objectively estimate the hazards that such environments pose to hearing. This proposal is directed toward such a goal. Specifically we propose: (1) to design a model digital noise generation system which can be used to reproduce the essential characteristics of high level "complex" industrial noise environments. The system will incorporate features such as multiple impact sources and room reflection characteristics to produce "complex" noises whose "Gaussian" and "non-Gaussian" components can be controlled; (2) to explore the applicability of new approaches to signal analysis that can be used to quantitatively evaluate non-Gaussian noise environments for the purpose of hearing conservation. Primary attention will focus upon methods which utilize adaptive noise cancellation, frequency domain kurtosis and cepstral analysis; (3) to initiate a series of animal exposure experiments using "complex noise" paradigms that are designed to explore which metrics of a "complex noise" environment are suitable predictors of the hazard to hearing following prolonged exposures. The rationale of this approach to the study of the hearing hazards associated with "complex noise" environments rests upon: (1) our experience that the parametric approach used in the past in our laboratories and in most other laboratories requires an inordinately large number of animal exposures and an extraordinary time consuming effort to understand the interrelations among the many variables associated with high level impact noise; (2) a recognition that the results of such a parametric approach, while they have been very instructive and valid at some levels of inquiry, are difficult to apply to realistic industrial noise environments where the temporal signals can be very difficult to evaluate even using sophisticated instrumentation and impossible to evaluate using much of the available conventional noise measuring equipment. Our approach will yield a generalized methodology that can be used to model and evaluate industrial noise environments and holds the promise for producing metrics that can be used to gauge the hazards posed by virtually any industrial noise environment.